Remote Sensing
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Remote Sens. 2015, 7, 14360-14385; doi:10.3390/rs71114360 OPEN ACCESS remote sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Article Satellite Images for Monitoring Mangrove Cover Changes in a Fast Growing Economic Region in Southern Peninsular Malaysia Kasturi Devi Kanniah 1,*, Afsaneh Sheikhi 1, Arthur P. Cracknell 2, Hong Ching Goh 3, Kian Pang Tan 1, Chin Siong Ho 4 and Fateen Nabilla Rasli 1 1 TropicalMap Research Group, Faculty of Geoinformation and Real Estate, UTM Palm Oil Research Centre, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia; E-Mails: [email protected] (A.S.); [email protected] (K.P.T.); [email protected] (F.N.R.) 2 Division of Electronic Engineering and Physics, University of Dundee, Dundee DDI 4HN, UK; E-Mail: [email protected] 3 Department of Urban and Regional Planning, Faculty of Built Environment, Universiti Malaya, Kuala Lumpur 50603, Malaysia; E-Mail: [email protected] 4 Department of Urban and Regional Planning, Faculty of Built Environment, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia; E-Mail: [email protected] * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +60-7553-0851 (ext. 30851). Academic Editors: Chandra Giri, Ioannis Gitas and Prasad S. Thenkabail Received: 6 August 2015 / Accepted: 13 October 2015 / Published: 29 October 2015 Abstract: Effective monitoring is necessary to conserve mangroves from further loss in Malaysia. In this context, remote sensing is capable of providing information on mangrove status and changes over a large spatial extent and in a continuous manner. In this study we used Landsat satellite images to analyze the changes over a period of 25 years of mangrove areas in Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia. We tested the use of two widely used digital classification techniques to classify mangrove areas. The Maximum Likelihood Classification (MLC) technique provided significantly higher user, producer and overall accuracies and less “salt and pepper effects” compared to the Support Vector Machine (SVM) technique. The classified satellite images using the MLC technique showed that IM lost 6740 ha of mangrove areas from 1989 to 2014. Nevertheless, a gain of 710 ha of Remote Sens. 2015, 7 14361 mangroves was observed in this region, resulting in a net loss of 6030 ha or 33%. The loss of about 241 ha per year of mangroves was associated with a steady increase in urban land use (1225 ha per year) from 1989 until 2014. Action is necessary to protect the existing mangrove cover from further loss. Gazetting of the remaining mangrove sites as protected areas or forest reserves and introducing tourism activities in mangrove areas can ensure the continued survival of mangroves in IM. Keywords: land cover change; mangroves; Iskandar Malaysia; remote sensing; maximum likelihood classifier; support vector machine 1. Introduction Mangrove ecosystems are found in many sub-tropical and tropical areas of the world including Malaysia (Figure 1) and they are growing along sheltered coastlines such as river estuaries or tidal marshes [1]. The various goods and services provided by these forests make them one of the valuable ecosystems in the world. Although mangroves constitute less than 0.4% of the world’s forests [2], they play an important role in providing habitats for thousands of marine and pelagic species, and serving the local communities with food, medicine, fuel and building materials. They also become important in mitigating the impact of climate change by sequestering CO2 (the main greenhouse gas, apart from water vapor) from the atmosphere as they are one of the most carbon-rich forests in the tropics [3–5]. They also protect the coastal areas from tidal waves, tsunamis and cyclones [6]. Figure 1. Mangrove forests distribution in Malaysia (Upper left: 7°22′46″N, 98°55′48″E and Lower right: 0°51′10″N, 119°16′00″E) [7]. Despite their significance in providing ecological and economic services, mangroves are being lost at the rate of about 1% per year globally [8]. The rate of loss is highest in developing countries and in Malaysia the rate is estimated to be about 1% or 1282 ha·year−1 since 1990 [9]. Mangroves are cleared Remote Sens. 2015, 7 14362 for coastal development, aquaculture, timber and fuel production [10]. Similar to the urbanization at global level, the southern coast of Johor-Iskandar Malaysia (IM) (Figure 2a) is undergoing the highest economic growth rate in the country. The fast pace urbanization threatens the survival of mangrove forests. In fact, Johor experienced the third largest mangrove loss after Selangor and Pahang states in Malaysia [11]. Mangrove forests in IM are continuously being cleared for constructing housing and industrial buildings, ports, power plants, oil storage, and a coastal way via massive reclamation works and also being transformed into urban water fronts. Continuous loss of mangroves in this region will have a negative impact on environmental stability and on aquatic organisms and the biodiversity of the flora and fauna. Thus, an effective monitoring of mangrove forest is urgently required to prevent further loss of mangroves in Johor. Ground surveying methods and field observations are traditionally used for mapping mangrove areas. Although this can provide good mapping accuracy (cm to m), it is rather time consuming, laborious and costly; moreover, this method is not practical in a harsh mangrove environment that is temporarily inundated and hard to access [11,12]. Tidal change in mangrove areas makes the area change assessment more difficult by the inventory method. In past decades, multi temporal aerial photographs with high spatial resolution (<1 m) provided a local to sub-regional scale mapping and monitoring of mangrove ecosystems [13–15]. However, the potential for obtaining good images depends on flight and local weather conditions. Alternatively, remote sensing technology that delivers satellite images covering large-spatial scale, on a continuous basis (long-term) and at reduced cost can provide up to date information on mangrove forests, their spatio-temporal changes and the mangrove trees’ health conditions. This information will provide economists, ecologists, and natural resources managers in Malaysia with valuable information to improve management strategies for mangrove ecosystems. Remote sensing data and methods have been applied widely for mapping mangrove ecosystem distribution, species differentiation, health status, and changes of mangrove populations [1,11,16]. Satellite data with fine to medium spatial resolution such as Ikonos, Quickbird, and Landsat Thematic Mapper can provide adequate spatial details for mapping mangroves areas [16]. Meanwhile, hyperspectral images are useful in discriminating various mangrove species [17]. In Malaysia mangrove ecosystems have been studied using various remote sensing data for mangrove detection/areal delineation [18–21], mangrove change detection [22–29], mangrove species classification [21,30,31], and biomass of mangrove forest [5]. Change detection of mangrove areas using satellite data has been conducted in Malaysia at a local scale. However a detailed analysis of the Iskandar Malaysia region, using consistent data sources and methodology and suitable spatial and temporal scales, was not available. Thus, the overall goal of this study was to evaluate satellite imagery as a tool for monitoring changes in mangrove forests in Iskandar Malaysia and the secondary objective was to evaluate training sample size on classification accuracy. Both Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) techniques were employed to classify mangrove and other land cover types in IM using time series Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) data. We then detected the changes in the land cover over a period of 25 years (1989–2014). Such studies are important for the development of a regional action plan in conserving mangrove resources in Malaysia. Remote Sens. 2015, 7 14363 Figure 2. (a) The Iskandar Malaysia (IM) region in Johor State of Peninsular Malaysia shown by a Landsat image (the five flagship zones are marked as A–E); and (b) the three Ramsar sites in IM (source: Comprehensive Development Plan ii—unpublished). 2. Study Area The total global coverage of mangrove forests is 15.62 Mha and of this 3.7% is found in Malaysia. Mangroves are established mostly along the west coast of Peninsular Malaysia and in the states of Sabah and Sarawak in Malaysian Borneo (Figure 1). Mangroves in Peninsular Malaysia constitute about 17% of the total mangroves of Malaysia (0.58 Mha) and the rest are found in Eastern Malaysia in Sabah (58.6%) and Sarawak (24.4%). The main mangrove tree species found in Malaysia are from the Rhizophoraceae family. However, there are at least a total of 70 mangroves species from 28 families that are found in this country [7]. Mangroves in Malaysia provide various ecological, economic and social benefits to the people and country [12]. This study focuses on Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia (Figure 2a). It was established in 2006 to bring in more focused economic and infrastructure investments and the region is administered by the Iskandar Regional Development Authority (IRDA). The region encompasses an area of 2217 km2; it involves five local Remote Sens.